Add fixed start and end to TSP solver and option to select optimizati…

…on method in gpflow

setup.py

@@ -1,6 +1,6 @@
 from setuptools import setup, find_packages
 
-__version__ = "1.0.1"
+__version__ = "1.0.2"
 
 setup(
  name='sgptools',

sgptools/utils/gpflow.py

@@ -92,6 +92,7 @@ def optimize_model(model,
  kernel_grad=True, 
  lr=1e-2, 
  optimizer='tf', 
+ method=None,
  verbose=False, 
  trace_fn=None,
  convergence_criterion=True,
@@ -106,6 +107,7 @@ def optimize_model(model,
  kernel_grad (bool): If False, the kernel parameters will not be optimized
  lr (float): Optimization learning rate
  optimizer (str): Optimizer to use for training (`scipy` or `tf`)
+ method (str): Optimization method refer to scipy minimize and tf optimizers for full list
  verbose (bool): If true, the training progress will be printed
  trace_fn (str): Function to trace metrics during training. 
  If `None`, the loss values are traced;
@@ -123,9 +125,12 @@ def optimize_model(model,
  trainable_variables=model.trainable_variables[:1]
 
  if optimizer == 'scipy':
+ if method is None:
+ method = 'L-BFGS-B'
  opt = gpflow.optimizers.Scipy()
  losses = opt.minimize(model.training_loss,
  trainable_variables,
+ method=method,
  options=dict(disp=verbose, maxiter=max_steps),
  tol=tol)
  losses = losses.fun
@@ -136,7 +141,10 @@ def optimize_model(model,
  def trace_fn(traceable_quantities):
  return model.inducing_variable.Z.numpy()
 
- opt = tf.keras.optimizers.Adam(lr)
+ if method is None:
+ method = 'adam'
+ opt = tf.keras.optimizers.get(method)
+ opt.learning_rate = lr
  loss_fn = model.training_loss
  if convergence_criterion:
  convergence_criterion = tfp.optimizer.convergence_criteria.LossNotDecreasing(

sgptools/utils/tsp.py

@@ -19,7 +19,13 @@
 import numpy as np
 
 
-def run_tsp(nodes, num_vehicles=1, max_dist=25, depth=1, resample=None):
+def run_tsp(nodes, 
+ num_vehicles=1, 
+ max_dist=25, 
+ depth=1, 
+ resample=None, 
+ start_idx=None,
+ end_idx=None):
  """Method to run TSP/VRP with arbitrary start and end nodes, 
  and without any distance constraint
  
@@ -30,6 +36,8 @@ def run_tsp(nodes, num_vehicles=1, max_dist=25, depth=1, resample=None):
  depth (int): Internal parameter used to track re-try recursion depth
  resample (int): Each solution path will be resampled to have
  `resample` number of points
+ start_idx (list): Optionl list of start node indices from which to start the solution path 
+ end_idx (list): Optionl list of end node indices from which to start the solution path 
 
  Returns:
  paths (ndarray): Solution paths
@@ -38,13 +46,28 @@ def run_tsp(nodes, num_vehicles=1, max_dist=25, depth=1, resample=None):
  if depth > 5:
  print('Warning: Max depth reached')
  return None, None
-
+ 
  # Add dummy 0 location to get arbitrary start and end node sols
- distance_mat = np.zeros((len(nodes)+1, len(nodes)+1))
- distance_mat[1:, 1:] = pairwise_distances(nodes, nodes)*1e4
+ if start_idx is None or end_idx is None:
+ distance_mat = np.zeros((len(nodes)+1, len(nodes)+1))
+ distance_mat[1:, 1:] = pairwise_distances(nodes, nodes)*1e4
+ trim_paths = True
+ else:
+ distance_mat = pairwise_distances(nodes, nodes)*1e4
+ trim_paths = False
  distance_mat = distance_mat.astype(int)
  max_dist = int(max_dist*1e4)
 
+ if start_idx is None:
+ start_idx = [0]*num_vehicles
+ elif trim_paths:
+ start_idx = [i+1 for i in start_idx]
+
+ if end_idx is None:
+ end_idx = [0]*num_vehicles
+ elif trim_paths:
+ end_idx = [i+1 for i in end_idx]
+
  def distance_callback(from_index, to_index):
  from_node = manager.IndexToNode(from_index)
  to_node = manager.IndexToNode(to_index)
@@ -53,8 +76,8 @@ def distance_callback(from_index, to_index):
  # num_locations, num vehicles, start, end
  manager = pywrapcp.RoutingIndexManager(len(distance_mat), 
  num_vehicles, 
- [0]*num_vehicles, 
- [0]*num_vehicles)
+ start_idx,
+ end_idx)
  routing = pywrapcp.RoutingModel(manager)
  transit_callback_index = routing.RegisterTransitCallback(distance_callback)
  routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
@@ -79,12 +102,14 @@ def distance_callback(from_index, to_index):
  search_parameters.local_search_metaheuristic = (
  routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
  )
- search_parameters.time_limit.seconds = 5
+ search_parameters.time_limit.seconds = 10
  solution = routing.SolveWithParameters(search_parameters)
 
  paths = None
  if solution is not None:
- paths, distances = get_routes(manager, routing, solution, num_vehicles)
+ paths, distances = get_routes(manager, routing, 
+ solution, num_vehicles, 
+ start_idx, end_idx, trim_paths)
  for path in paths:
  if len(path) < 2:
  print('TSP Warning: Empty path detected')
@@ -107,7 +132,7 @@ def distance_callback(from_index, to_index):
 '''
 Method to extract route from or-tools solution
 '''
-def get_routes(manager, routing, solution, num_vehicles):
+def get_routes(manager, routing, solution, num_vehicles, start_idx, end_idx, trim_paths):
  paths = []
  distances = []
  for vehicle_id in range(num_vehicles):
@@ -124,7 +149,13 @@ def get_routes(manager, routing, solution, num_vehicles):
  path.append(manager.IndexToNode(index))
  distances.append(route_distance)
  # remove dummy start/end point
- paths.append(np.array(path)[1:-1]-1)
+ if trim_paths:
+ path = np.array(path)-1
+ if start_idx[vehicle_id] == 0:
+ path = path[1:]
+ if end_idx[vehicle_id] == 0:
+ path = path[:-1]
+ paths.append(path)
  return paths, distances
 
 def resample_path(waypoints, num_inducing=10):